我正在尝试为Kaggle上的生物医学项目构建一个用于256×256 nifti-1文件的U-Net模型。当我使用128×128时，一切都运行得很完美。现在我遇到了一个错误，我不知道问题出在哪里，请帮助我解决这个问题。

inputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))s = tf.keras.layers.Lambda(lambda x: x / 255)(inputs)#收缩路径c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s)c1 = tf.keras.layers.Dropout(0.1)(c1)c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)c2 = tf.keras.layers.Dropout(0.1)(c2)c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2) c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)c3 = tf.keras.layers.Dropout(0.2)(c3)c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3) c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)c4 = tf.keras.layers.Dropout(0.2)(c4)c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4) c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)c5 = tf.keras.layers.Dropout(0.3)(c5)c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)p5 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)c6 = tf.keras.layers.Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p5)c6 = tf.keras.layers.Dropout(0.3)(c6)c6 = tf.keras.layers.Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)#扩展路径 u7 = tf.keras.layers.Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(c6)

!!!!这一行出错

u7 = tf.keras.layers.concatenate([u7, c5])

c7 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)c7 = tf.keras.layers.Dropout(0.2)(c7)c7 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)u8 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c7)u8 = tf.keras.layers.concatenate([u8, c4])c8 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)c8 = tf.keras.layers.Dropout(0.2)(c8)c8 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8) u9 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c8)u9 = tf.keras.layers.concatenate([u9, c3])c9 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)c9 = tf.keras.layers.Dropout(0.2)(c9)c9 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9) u10 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c9)u10 = tf.keras.layers.concatenate([u10, c2])c10 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u10)c10 = tf.keras.layers.Dropout(0.1)(c10)c10 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c10) u11 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c10)u11 = tf.keras.layers.concatenate([u11, c1], axis=3)c11 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u11)c11 = tf.keras.layers.Dropout(0.1)(c11)c11 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c11)

回答：

在定义p5的Max Pool 2D时有拼写错误。请改为：

p5 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c5)